• Science of Emotion and Sensibility
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• v.10 no.2
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• pp.147-154
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• 2007

Electrocardiogram(ECG) being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm in the con due to inaccuracy of domestic diagnosis results for a heart disease. This paper proposes ST-segment extraction technique diagnosing heart disease parameter from raw ECG data. As the ST-segment is used for prediction of Coronary Artery Disease, we can predict heart disease using classification approach in data mining technique. We can also predict patient's clinical characterization from patient clinical data.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.223-229
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• 2022

Early detection can help prevent heart disease, which is one of the most common reasons for death. This paper provides a clinical support model for predicting cardiac disease. The model is built using two publicly available data sets. The admissibility and application of the the model are justified by a sequence of tests. Implementation of the model and testing are also discussed

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• The Korean Journal of Nuclear Medicine
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• v.38 no.5
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• pp.331-337
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• 2004

Cardiac neurotransmission imaging allows in vivo assessment of presynaptic reuptake, neurotransmitter storage and postsynaptic receptors. Among the various neurotransmitter, I-123 MIBG is most available and relatively well-established. Metaiodobenzylguanidine (MIBG) is an analogue of the false neurotransmitter guanethidine. It is taken up to adrenergic neurons by uptake-1 mechanism as same as norepinephrine. As tagged with I-123, it can be used to image sympathetic function in various organs including heart with planar or SPECT techniques. I-123 MIBG imaging has a unique advantage to evaluate myocardial neuronal activity in which the heart has no significant structural abnormality or even no functional derangement measured with other conventional examination. In patients with cardiomyopathy and heart failure, this imaging has most sensitive technique to predict prognosis and treatment response of betablocker or ACE inhibitor. In diabetic patients, it allow very early detection of autonomic neuropathy. In patients with dangerous arrhythmia such as ventricular tachycardia or fibrillation, MIBG imaging may be only an abnormal result among various exams. In patients with ischemic heart disease, sympathetic derangement may be used as the method of risk stratification. In heart transplanted patients, sympathetic reinnervation is well evaluated. Adriamycin-induced cardiotoxicity is detected earlier than ventricular dysfunction with sympathetic dysfunction. Neurodegenerative disorder such as Parkinson's disease or dementia with Lewy bodies has also cardiac sympathetic dysfunction. Noninvasive assessment of cardiac sympathetic nerve activity with I-123 MIBG imaging nay be improve understanding of the pathophysiology of cardiac disease and make a contribution to predict survival and therapy efficacy.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.514-523
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• 2024

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.

• Journal of Preventive Medicine and Public Health
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• v.45 no.5
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• pp.323-328
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• 2012

Objectives: Economic growth and development of medical technology help to improve the average life expectancy, but the western diet and rapid conversions to poor lifestyles lead an increasing risk of major chronic diseases. Coronary heart disease mortality in Korea has been on the increase, while showing a steady decline in the other industrialized countries. An age-period-cohort analysis can help understand the trends in mortality and predict the near future. Methods: We analyzed the time trends of ischemic heart disease mortality, which is on the increase, from 1985 to 2009 using an age-period-cohort model to characterize the effects of ischemic heart disease on changes in the mortality rate over time. Results: All three effects on total ischemic heart disease mortality were statistically significant. Regarding the period effect, the mortality rate was decreased slightly in 2000 to 2004, after it had continuously increased since the late 1980s that trend was similar in both sexes. The expected age effect was noticeable, starting from the mid-60's. In addition, the age effect in women was more remarkable than that in men. Women born from the early 1900s to 1925 observed an increase in ischemic heart mortality. That cohort effect showed significance only in women. Conclusions: The future cohort effect might have a lasting impact on the risk of ischemic heart disease in women with the increasing elderly population, and a national prevention policy is need to establish management of high risk by considering the age-period-cohort effect.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.40-45
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• 2017

This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.

• Journal of Chest Surgery
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• v.30 no.12
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• pp.1205-1213
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• 1997

This study reviewed useful aspects of the intraoperative transesophageal echocardiography among the patients in whom heart surgery were undertaken between January 1996 and July 1996 at St.Pauls hospital, Medical College of Catholic University, Seoul, Korea. During that period, 61 patients were operated on because of valvular heart disease(25 patients), coronary artery disease(22 patients), congenital heart disease(13 patients), and combined coronary artery disease and valvular heart disease(1 patient). Two patients(1 redo-VSD and 1 valvular heart diease) needed repeated aortic cross clamping and complementary procedures because of incomplete initial procedures. There was no incidence of air embolism. We could observe significant relationship of cardiac output monitoring methods either by thermodilution technique and transesophageal echocardiography by linear regression analysis(p<0.001). We tested myocardial response(percentage of systolic wall thickness, PSWT) with low dose dobutamine challenge to predict post-CABG myocardial perfusion. And the test showed statistically significant resp.onse(sensitivity 76%, specificity 94.7%, positive predictive value 95%, negative predictive value 75%). These results suggest that cardiac surgeon could draw more benefits by intraoperative transesophageal echocardiography.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.21-28
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• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

• Journal of radiological science and technology
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• v.47 no.2
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• pp.125-135
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• 2024

This study analysed the factors that predict and influence heart disease through key indicators related to changes in left atrial and left ventricular size. Measurements recommended by the American Society of Echocardiography were used, and the influence of variables was assessed using multiple regression analysis. The results showed that left atrial volume index(LAVI) was significantly different by age, obesity, diabetes, hypertension, dyslipidaemia, and left ventricular relaxation dysfunction(p<0.05). Left ventricular mass index(LVMI) was significantly different according to age, body mass index, hypertension, diabetes, dyslipidaemia, and left ventricular relaxation dysfunction(p<0.05). Increases in LVMI and relative ventricular wall thickness(RWT) were associated with changes in LAVI(p<0.05). Age, systolic blood pressure, increased LAVI, and RWT influenced changes in LVMI, and left ventricular dysfunction was analysed as an influencing factor for both changes in LAVI and LVMI. Therefore, changes in left atrial and left ventricular size are indicators for early diagnosis and prevention of heart disease, and it is necessary to carefully observe structural changes in the heart and actively manage risk factors for the prevention and management of heart disease.